"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[str]									=			{
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					int							  =						"""beit"""


 def __init__( self    ,		lowerCamelCase=8192    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-12    ,		lowerCamelCase=224    ,		lowerCamelCase=16    ,		lowerCamelCase=3    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=True    ,		lowerCamelCase=[3, 5, 7, 11]    ,		lowerCamelCase=[1, 2, 3, 6]    ,		lowerCamelCase=True    ,		lowerCamelCase=0.4    ,		lowerCamelCase=256    ,		lowerCamelCase=1    ,		lowerCamelCase=False    ,		lowerCamelCase=255    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase						)

        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    initializer_range
        __a       =    layer_norm_eps

        __a       =    image_size
        __a       =    patch_size
        __a       =    num_channels
        __a       =    use_mask_token
        __a       =    use_absolute_position_embeddings
        __a       =    use_relative_position_bias
        __a       =    use_shared_relative_position_bias
        __a       =    layer_scale_init_value
        __a       =    drop_path_rate
        __a       =    use_mean_pooling
        # decode head attributes (semantic segmentation)
        __a       =    out_indices
        __a       =    pool_scales
        # auxiliary head attributes (semantic segmentation)
        __a       =    use_auxiliary_head
        __a       =    auxiliary_loss_weight
        __a       =    auxiliary_channels
        __a       =    auxiliary_num_convs
        __a       =    auxiliary_concat_input
        __a       =    semantic_loss_ignore_index






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Optional[int]							  =						version.parse("""1.11"""       )


 @property
 def 						a__   ( self						):
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]						)






 @property
 def 						a__   ( self						):
        return 1E-4

"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
